Utilizing different data compression algorithms based on characteristics of a storage system

ABSTRACT

Utilizing different data compression algorithms based on characteristics of a storage system, including: selecting, in dependence upon a priority for conserving processing resources or storage resources in a storage system, a data compression algorithm to utilize to compress data; detecting that at least one of an amount of processing resources available in the storage system or the amount of space available to store additional data in the storage system has changed; and responsive to detecting that at least one of the amount of processing resources available in the storage system or the amount of space available to store additional data in the storage system has changed, selecting a different data compression algorithm to utilize to compress data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application for patent entitled to a filing dateand claiming the benefit of earlier-filed U.S. Pat. No. 11,392,565,issued Jul. 19, 2022, herein incorporated by reference in its entirety,which is a continuation of U.S. Pat. No. 10,572,460, issued Feb. 25,2020.

BACKGROUND Technical Field

The field of the disclosure is data processing, or, more specifically,methods, apparatus, and products for compressing data in dependence uponcharacteristics of a storage system.

Background Art

Enterprise storage systems frequently include a plurality of storagedevices. Each of the storage devices may be capable of storing aparticular amount of data, and as such, the storage system as a whole ischaracterized by the cumulative capacity of the storage devices thatmake up the storage system. In order to better utilize the capacity ofthe storage system, data reduction techniques are often applied toreduce the size of the data stored in the storage system. One suchtechnique is data compression. Data compression, however, is frequentlycarried out in an unsophisticated, non-optimal manner.

SUMMARY OF INVENTION

Methods, apparatuses, and products for compressing data in dependenceupon characteristics of a storage system, including: receiving an amountof processing resources available in the storage system; receiving anamount of space available in the storage system; and selecting, independence upon a priority for conserving the amount of processingresources and the amount of space, a data compression algorithm toutilize to compress the data.

The foregoing and other objects, features and advantages of thedisclosure will be apparent from the following more particulardescriptions of example embodiments of the disclosure as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the disclosure.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 sets forth a block diagram of a storage system configured forcompressing data in dependence upon characteristics of the storagesystem according to embodiments of the present disclosure.

FIG. 2 sets forth a block diagram of a storage array controller usefulin compressing data in dependence upon characteristics of a storagesystem according to embodiments of the present disclosure.

FIG. 3 sets forth a flow chart illustrating an example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 4 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 5 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 7 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating a further example method forcompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatuses, and products for compressing data independence upon characteristics of a storage system in accordance withthe present disclosure are described with reference to the accompanyingdrawings, beginning with FIG. 1 . FIG. 1 sets forth a block diagram of astorage system configured for compressing data in dependence uponcharacteristics of the storage system according to embodiments of thepresent disclosure. The storage system of FIG. 1 includes a number ofcomputing devices (164, 166, 168, 170). Such computing devices may beimplemented in a number of different ways. For example, a computingdevice may be a server in a data center, a workstation, a personalcomputer, a notebook, or the like.

The computing devices (164, 166, 168, 170) in the example of FIG. 1 arecoupled for data communications to a number of storage arrays (102, 104)through a storage area network (‘SAN’) (158) as well as a local areanetwork (160) (‘LAN’). The SAN (158) may be implemented with a varietyof data communications fabrics, devices, and protocols. Example fabricsfor such a SAN (158) may include Fibre Channel, Ethernet, Infiniband,Serial Attached Small Computer System Interface (‘SAS’), and the like.Example data communications protocols for use in such a SAN (158) mayinclude Advanced Technology Attachment (‘ATA’), Fibre Channel Protocol,SCSI, iSCSI, HyperSCSI, and others. Readers of skill in the art willrecognize that a SAN is just one among many possible data communicationscouplings which may be implemented between a computing device (164, 166,168, 170) and a storage array (102, 104). For example, the storagedevices (146, 150) within the storage arrays (102, 104) may also becoupled to the computing devices (164, 166, 168, 170) as networkattached storage (‘NAS’) capable of facilitating file-level access, oreven using a SAN-NAS hybrid that offers both file-level protocols andblock-level protocols from the same system. Any other such datacommunications coupling is well within the scope of embodiments of thepresent disclosure.

The local area network (160) of FIG. 1 may also be implemented with avariety of fabrics and protocols. Examples of such fabrics includeEthernet (802.3), wireless (802.11), and the like. Examples of such datacommunications protocols include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Real Time Protocol (‘RTP’) andothers as will occur to those of skill in the art.

The example storage arrays (102, 104) of FIG. 1 provide persistent datastorage for the computing devices (164, 166, 168, 170). Each storagearray (102, 104) depicted in FIG. 1 includes a storage array controller(106, 112). Each storage array controller (106, 112) may be embodied asa module of automated computing machinery comprising computer hardware,computer software, or a combination of computer hardware and software.The storage array controllers (106, 112) may be configured to carry outvarious storage-related tasks. Such tasks may include writing datareceived from the one or more of the computing devices (164, 166, 168,170) to storage, erasing data from storage, retrieving data from storageto provide the data to one or more of the computing devices (164, 166,168, 170), monitoring and reporting of disk utilization and performance,performing RAID (Redundant Array of Independent Drives) or RAID-likedata redundancy operations, compressing data, encrypting data, and soon.

Each storage array controller (106, 112) may be implemented in a varietyof ways, including as a Field Programmable Gate Array (‘FPGA’), aProgrammable Logic Chip (‘PLC’), an Application Specific IntegratedCircuit (‘ASIC’), or computing device that includes discrete componentssuch as a central processing unit, computer memory, and variousadapters. Each storage array controller (106, 112) may include, forexample, a data communications adapter configured to supportcommunications via the SAN (158) and the LAN (160). Although only one ofthe storage array controllers (112) in the example of FIG. 1 is depictedas being coupled to the LAN (160) for data communications, readers willappreciate that both storage array controllers (106, 112) may beindependently coupled to the LAN (160). Each storage array controller(106, 112) may also include, for example, an I/O controller or the likethat couples the storage array controller (106, 112) for datacommunications, through a midplane (114), to a number of storage devices(146, 150), and a number of write buffer devices (148, 152).

Each write buffer device (148, 152) may be configured to receive, fromthe storage array controller (106, 112), data to be stored in thestorage devices (146). Such data may originate from any one of thecomputing devices (164, 166, 168, 170). In the example of FIG. 1 ,writing data to the write buffer device (148, 152) may be carried outmore quickly than writing data to the storage device (146, 150). Thestorage array controller (106, 112) may be configured to effectivelyutilize the write buffer devices (148, 152) as a quickly accessiblebuffer for data destined to be written to storage. In this way, thelatency of write requests may be significantly improved relative to asystem in which the storage array controller writes data directly to thestorage devices (146, 150).

A ‘storage device’ as the term is used in this specification refers toany device configured to record data persistently. The term‘persistently’ as used here refers to a device's ability to maintainrecorded data after loss of a power source. Examples of storage devicesmay include mechanical, spinning hard disk drives, Solid-state drives(e.g., “Flash drives”), and the like.

The storage array controllers (106, 112) of FIG. 1 may be useful incompressing data in dependence upon characteristics of a storage systemaccording to embodiments of the present disclosure. The storage arraycontrollers (106, 112) may assist in intelligently compressing data byreceiving an amount of processing resources available in the storagesystem, receiving an amount of space available in the storage system,and selecting a data compression algorithm to utilize to compress thedata in dependence upon a priority for conserving the amount ofprocessing resources and the amount of space, and performing otherfunctions as will be described in greater detail below.

The arrangement of computing devices, storage arrays, networks, andother devices making up the example system illustrated in FIG. 1 are forexplanation, not for limitation. Systems useful according to variousembodiments of the present disclosure may include differentconfigurations of servers, routers, switches, computing devices, andnetwork architectures, not shown in FIG. 1 , as will occur to those ofskill in the art.

Compressing data in dependence upon characteristics of a storage systemin accordance with embodiments of the present disclosure is generallyimplemented with computers. In the system of FIG. 1 , for example, allthe computing devices (164, 166, 168, 170) and storage controllers (106,112) may be implemented to some extent at least as computers. Forfurther explanation, therefore, FIG. 2 sets forth a block diagram of astorage array controller (202) useful in compressing data in dependenceupon characteristics of a storage system according to embodiments of thepresent disclosure.

The storage array controller (202) of FIG. 2 is similar to the storagearray controllers depicted in FIG. 1 , as the storage array controller(202) of FIG. 2 is communicatively coupled, via a midplane (206), to oneor more storage devices (212) and to one or more memory buffer devices(214) that are included as part of a storage array (216). The storagearray controller (202) may be coupled to the midplane (206) via one ormore data communications links (204) and the midplane (206) may becoupled to the storage devices (212) and the memory buffer devices (214)via one or more data communications links (208, 210). The datacommunications links (204, 208, 210) of FIG. 2 may be embodied, forexample, as Peripheral Component Interconnect Express (‘PCIe’) bus.

The storage array controller (202) of FIG. 2 includes at least onecomputer processor (232) or ‘CPU’ as well as random access memory(‘RAM’) (236). The computer processor (232) may be connected to the RAM(236) via a data communications link (230), which may be embodied as ahigh speed memory bus such as a Double-Data Rate 4 (‘DDR4’) bus.

Stored in RAM (214) is an operating system (246). Examples of operatingsystems useful in storage array controllers (202) configured forintelligently compressing data in a storage array according toembodiments of the present disclosure include UNIX™, Linux, MicrosoftWindows™, and others as will occur to those of skill in the art. Alsostored in RAM (236) is a compression optimization module (248), a modulethat includes computer program instructions useful in compressing datain dependence upon characteristics of a storage system that includes aplurality of storage devices according to embodiments of the presentdisclosure.

The compression optimization module (248) may compress data independence upon characteristics of a storage system by: receiving anamount of processing resources available in the storage system,receiving an amount of space available in the storage system, andselecting, in dependence upon a priority for conserving the amount ofprocessing resources and the amount of space, a data compressionalgorithm to utilize to compress the data, as will be described ingreater detail below.

The compression optimization module (248) may further intelligentlycompress data in a storage array that includes a plurality of storagedevices by: determining an expected amount of space in the storagesystem to be consumed within a predetermined period of time, determiningan expected amount of processing resources to be consumed by compressingthe data utilizing the data compression algorithm for each of aplurality of data compression algorithms, determining an expected amountof data reduction to be achieved by compressing the data utilizing thedata compression algorithm for each of a plurality of data compressionalgorithms, determining, an expected decompression speed associated withdecompressing the data for each of a plurality of data compressionalgorithms, determining an average decompression speed associated withdecompressing a pool of data for each of a plurality of data compressionalgorithms, compressing at least a portion of the data utilizing aplurality of data compression algorithms, identifying a data reductionlevel achieved by each data compression algorithm, determining adecompression speed associated with each data compression algorithm, andselecting the data compression algorithm to utilize to compress the datain dependence upon the data reduction level achieved by each datacompression algorithm and the decompression speed associated with eachdata compression algorithm, as will be described in greater detailbelow.

The storage array controller (202) of FIG. 2 also includes a pluralityof host bus adapters (218, 220, 222) that are coupled to the processor(232) via a data communications link (224, 226, 228). Each host busadapter (218, 220, 222) may be embodied as a module of computer hardwarethat connects the host system (i.e., the storage array controller) toother network and storage devices. Each of the host bus adapters (218,220, 222) of FIG. 2 may be embodied, for example, as a Fibre Channeladapter that enables the storage array controller (202) to connect to aSAN, as an Ethernet adapter that enables the storage array controller(202) to connect to a LAN, and so on. Each of the host bus adapters(218, 220, 222) may be coupled to the computer processor (232) via adata communications link (224, 226, 228) such as, for example, a PCIebus.

The storage array controller (202) of FIG. 2 also includes a host busadapter (240) that is coupled to an expander (242). The expander (242)depicted in FIG. 2 may be embodied as a module of computer hardwareutilized to attach a host system to a larger number of storage devicesthan would be possible without the expander (242). The expander (242)depicted in FIG. 2 may be embodied, for example, as a SAS expanderutilized to enable the host bus adapter (240) to attach to storagedevices in an embodiment where the host bus adapter (240) is embodied asa SAS controller.

The storage array controller (202) of FIG. 2 also includes a switch(244) that is coupled to the computer processor (232) via a datacommunications link (238). The switch (244) of FIG. 2 may be embodied asa computer hardware device that can create multiple endpoints out of asingle endpoint, thereby enabling multiple devices to share what wasinitially a single endpoint. The switch (244) of FIG. 2 may be embodied,for example, as a PCIe switch that is coupled to a PCIe bus (238) andpresents multiple PCIe connection points to the midplane (206).

The storage array controller (202) of FIG. 2 also includes a datacommunications link (234) for coupling the storage array controller(202) to other storage array controllers. Such a data communicationslink (234) may be embodied, for example, as a QuickPath Interconnect(‘QPI’) interconnect, as PCIe non-transparent bridge (‘NTB’)interconnect, and so on.

Readers will recognize that these components, protocols, adapters, andarchitectures are for illustration only, not limitation. Such a storagearray controller may be implemented in a variety of different ways, eachof which is well within the scope of the present disclosure.

For further explanation, FIG. 3 sets forth a flow chart illustrating anexample method for compressing data in dependence upon characteristicsof a storage system (300) according to embodiments of the presentdisclosure. Although depicted in less detail, the storage system (300)of FIG. 3 may be similar to the storage arrays described above withreference to FIG. 1 . The storage system (300) of FIG. 3 includes aplurality of storage devices (302, 304, 306), each of which may be usedto store data (308, 310, 312).

The example method depicted in FIG. 3 is carried out, at least in part,by a compression prioritization module (314). The compressionprioritization module (314) may be embodied, for example, as a module ofcomputer software executing on computer hardware such as a computerprocessor. The compression prioritization module (314) may be executing,for example, on a storage array controller such as the storage arraycontrollers that are described above with reference to FIG. 1 and FIG. 2.

The example method depicted in FIG. 3 also includes receiving (316) anamount of processing resources (322) available in the storage system(300). The amount of processing resources (322) available in the storagesystem (300) may include, for example, a number of CPU processing cyclesthat are available per unit of time, a percentage of all of the CPUprocessing cycles that are available, an amount of memory that isavailable, and so on. The amount of processing resources (322) availablein the storage system (300) may be received (316), for example, by aresource monitoring module (326) that tracks resource utilization in thesystem, by the compression prioritization module (314) itself, or by anyother module that is included in the storage system (300) or operable tootherwise manage the storage system (300). Readers will appreciate thatthe amount of processing resources (322) available in the storage system(300) may be determined based on historical information, based on thecurrent load (e.g., the number of IOPS directed to the storage system)of the computing system (300) and the hardware resources of thecomputing system (300), based on any other indicia of the amount ofprocessing resources (322) available in the storage system (300) thatwill occur to those of skill in the art in view of the teachingscontained in this disclosure, or based on any combination thereof. Assuch, the amount of processing resources (322) available in the storagesystem (300) may be speculatively determined based on past systemperformance, current system performance, or any combination thereof.

The example method depicted in FIG. 3 also includes receiving (318) anamount of space (324) available in the storage system (300). The amountof space (324) available in the storage system (300) can represent thecumulative amount of available storage on the storage devices (302, 304,306) in the storage system (300). The amount of space (324) available inthe storage system (300) may be received (318) by the compressionprioritization module (314), for example, by a resource monitoringmodule (326) that tracks resource utilization in the system, by thecompression prioritization module (314) itself, or by any other modulethat is included in the storage system (300) or operable to otherwisemanage the storage system (300).

Readers will appreciate that the amount of space (324) available in thestorage system (300) is distinct from the amount of available memorythat may be included as part of the amount of processing resources (322)available in the storage system (300). The amount of space (324)available in the storage system (300) represents the amount oflong-term, persistent storage that is available within the storagedevices (302, 304, 306) that are included in the storage system (300).The amount of space (324) that is available in the storage system (300)may include not only space that is available in the traditional sense,but also space that is currently in use but may be reclaimed through theuse of one or more data reduction techniques such as garbage collection.In contrast to the amount of space (324) available in the storage system(300), the amount of available memory that may be included as part ofthe amount of processing resources (322) available in the storage system(300) represents short-term, possibly volatile, memory that is availablefor use in performing data compression operations. The amount ofavailable memory that may be included as part of the amount ofprocessing resources (322) available in the storage system (300) may beembodied, for example, as DRAM within a storage array controller such asthe storage array controllers described above with reference to FIG. 1and FIG. 2 . Readers will appreciate that when a user of the storagesystem (300) initiates a request to write data, the data will ultimatelybe stored within the long-term, persistent storage that is availablewithin the storage devices (302, 304, 306), not within the short-term,possibly volatile, memory that is available for use in performing datacompression operations.

The example method depicted in FIG. 3 also includes selecting (320), independence upon a priority for conserving the amount of processingresources and the amount of space, a data compression algorithm (328) toutilize to compress the data (308, 310, 312). Examples of datacompression algorithms (328) that may be utilized to compress the datacan include Lempel-Ziv algorithms, Burrows—Wheeler transform algorithms,dictionary coder algorithms, and many others. The priority forconserving the amount of processing resources and the amount of spacemay be embodied, for example, as a quantifiable value that represents apreference for conserving processing resources relative to conservingstorage resources. For example, the priority may be a level, percentage,or a number representing a balance between processing resources andspace. If the priority is determined to be biased towards betterutilization of processing resources then a compression algorithm whichachieves this outcome will be selected. In this scenario, the amount ofspace may or may not be optimal in order to receive better utilizationof processing resources. If the priority is determined to be biasedtowards better allocation of space then similarly a compressionalgorithm will be selected. In this scenario, the amount of processingresources may not be optimal at times and may also vary. In somecircumstances, a proportional optimization or balance between the amountof processing resources and amount of space is desired. The priority isplaced on achieving an objective towards conserving both processingresources and space. In all of these scenarios, one or more compressionalgorithms may be selected over a period of time. Conservation ofprocessing resources and space could be embodied as improvements tousage (e.g. less compute, memory, etc.), better utilization (e.g. use ofless space, balanced usage of processors, etc.), more efficient use ofenergy, and performance.

Such a priority may be dynamic as the value changes over time and such apriority may be calculated based on additional factors and could changeat different times. For example, one factor that impacts the prioritymay be the current operating conditions of the system (e.g., processingresources currently available in the storage system, the amount of spacecurrently available in the storage system, the rate at which consumptionof each type of resource is accelerating or decelerating), anotherfactor that impacts the priority may be historical operating conditionsof the system (e.g., times and dates at which consumption of each typeof resource spikes or decreases), another factor that impacts thepriority could be system settings (e.g., a general preference to run outof processing resources rather than storage resources, or vice versa),and so on.

In the example method depicted in FIG. 3 , selecting (320) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) in dependence upon a priority for conserving the amount ofprocessing resources and the amount of space may be carried out, forexample, by applying one or more predetermined formulas that utilizes apriority for conserving the amount of processing resources and theamount of space as an input. Such predetermined formulas may beutilized, for example, to generate a score for each available datacompression algorithm, where the data compression algorithm with thelowest or highest score (depending on the particular construction of thepredetermined formulas) is ultimately selected (320).

Readers will appreciate that the predetermined formula may be configuredto strike a balance between the amount of processing resources (322)available in the storage system (300) and the amount of space (324)available in the storage system. For example, when the amount ofprocessing resources (322) available in the storage system (300) arerelatively low, the compression prioritization module (314) may select(320) to compress the data (308, 310, 312) utilizing quick, lightweightcompression algorithms that consume relatively small amounts ofcomputing resources and also produce relatively small amounts of datareduction. Alternatively, when the amount of processing resources (322)available in the storage system (300) are relatively large, thecompression prioritization module (314) may select (320) to compress thedata (308, 310, 312) slower, heavier compression algorithms that consumerelatively large amounts of computing resources and also producerelatively large amounts of data reduction. When the amount of space(324) available in the storage system is relatively low, however, thecompression prioritization module (314) may select (320) to compress thedata (308, 310, 312) using slower, heavier compression algorithms thatconsume relatively large amounts of computing resources and also producerelatively large amounts of data reduction, as a premium will be placedon data reduction rather than conservation of processing resources insuch situations. Alternatively, when the amount of space (324) availablein the storage system is relatively high, a premium will be placedconserving processing resources rather than data reduction in suchsituations.

For further explanation, FIG. 4 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 4 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 4 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

The example method depicted in FIG. 4 also includes determining (402) anexpected amount of space (404) in the storage system (300) to beconsumed within a predetermined period of time. The compressionprioritization module (314) of FIG. 4 may determine (402) an expectedamount of space (404) in the storage system (300) to be consumed withina predetermined period of time, for example, through the use ofhistorical utilization information. If the compression prioritizationmodule (314) analyzes historical utilization information to determine,for example, that 30 GB of storage within the storage system (300) hasbeen consumed within the last 30 days, the compression prioritizationmodule (314) may determine that users of the storage system (300) willcontinue to consume storage within the storage system (300) at a rate of1 GB/day. In such a way, the expected amount of space (404) in thestorage system (300) to be consumed within a predetermined period oftime may be determined (402) by multiplying the rate at which storagewithin the storage system (300) has been consumed by the predeterminedperiod of time. The compression prioritization module (314) mayadditionally be configured to identify trends in usage (e.g., that therate at which storage is being consumed is increasing or decreasing at aspecific rate) and may therefore factor such trends into such adetermination (402). Readers will appreciate that determining (402) theexpected amount of space (404) in the storage system (300) to beconsumed within a predetermined period of time may be carried out inadditional ways utilizing historical information, real-time data, or anycombination thereof.

In the example method depicted in FIG. 4 , determining (402) theexpected amount of space (404) in the storage system (300) to beconsumed within a predetermined period of time may be carried out by thecompression prioritization module (314) receiving one or more messagesdescribing the utilization of storage space within the storage system(300). The one or more messages describing the utilization of storagespace within the storage system (300) may be received, for example, froma resource monitoring module (326) that tracks storage utilization inthe storage system (300) or by any other module that is included in thestorage system (300) or operable to otherwise manage the storage system(300). Alternatively, the utilization of storage space within thestorage system (300) may be tracked by the compression prioritizationmodule (314) itself.

In the example method depicted in FIG. 4 , the data compressionalgorithm (328) to utilize is further selected (406) in dependence uponthe expected amount of space (404) in the storage system (300) to beconsumed within the predetermined period of time. Selecting (406) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) in dependence upon the expected amount of space (404) in thestorage system (300) to be consumed within the predetermined period oftime may be carried out, for example, by applying one or morepredetermined formulas that utilizes the expected amount of space (404)in the storage system (300) to be consumed within the predeterminedperiod of time as an input. Such predetermined formulas may be utilized,for example, to generate a score for each available data compressionalgorithm, where the data compression algorithm with the lowest orhighest score (depending on the particular construction of thepredetermined formulas) is ultimately selected (406).

Readers will appreciate that when the expected amount of space (404) inthe storage system (300) to be consumed within the predetermined periodof time is relatively high, a data compression algorithm that canachieve higher levels of data reduction may be selected (406) even ifsuch a data compression algorithm consumes relatively large amounts ofprocessing resources, given that the available storage within thestorage system (300) is being rapidly consumed. Alternatively, when theexpected amount of space (404) in the storage system (300) to beconsumed within the predetermined period of time is relatively low, adata compression algorithm that consumes relatively small amounts ofprocessing resources may be selected (406) even if such a datacompression algorithm only achieves relatively small levels of datareduction, given that the available storage within the storage system(300) is not being rapidly consumed. In such a way, data compressionalgorithms that achieve higher levels of data reduction may be selected(406) as the expected amount of space (404) in the storage system (300)to be consumed within the predetermined period of time increases, evenif selecting such data compression algorithms requires a larger amountof processing resources to execute relative to data compressionalgorithms that achieve lower levels of data reduction.

For further explanation, FIG. 5 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 5 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 5 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

The example method depicted in FIG. 5 also includes determining (502),for each of a plurality of data compression algorithms, an expectedamount of processing resources (504) to be consumed by compressing thedata (308, 310, 312) utilizing the data compression algorithm. Thecompression prioritization module (314) of FIG. 5 may determine (502) anexpected amount of processing resources (504) to be consumed bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm, for example, through the use of historical informationgathered when previously compressing data. If the compressionprioritization module (314) analyzes historical information gatheredwhen previously compressing data to determine, for example, that a firstcompression algorithm requires 10,000 processing cycles to compress 1 GBof data, that a second compression algorithm requires 15,000 processingcycles to compress 1 GB of data, and that a third compression algorithmrequires 25,000 processing cycles to compress 1 GB of data. In such away, the expected amount of processing resources (504) to be consumed bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm may be determined (402) by multiplying the rate at whichprocessing resources are historically used to compress data by theamount of data to be compressed.

In the example method depicted in FIG. 5 , determining (502) theexpected amount of processing resources (504) to be consumed bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm may be carried out by the compression prioritization module(314) receiving one or more messages describing the historicalperformance of each data compression algorithm. The one or more messagesdescribing the historical performance of each data compression algorithmmay be received, for example, from a resource monitoring module (326)that tracks the performance of various compression algorithms or by anyother module that is included in the storage system (300) or operable tootherwise manage the storage system (300). Alternatively, the historicalperformance of each data compression algorithm may be tracked by thecompression prioritization module (314) itself.

In the example method depicted in FIG. 5 , the data compressionalgorithm (328) to utilize is further selected (506) in dependence uponthe expected amount of processing resources (504) to be consumed bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm. Selecting (506) a data compression algorithm (328) to utilizeto compress the data (308, 310, 312) in dependence upon the expectedamount of processing resources (504) to be consumed by compressing thedata (308, 310, 312) utilizing the data compression algorithm may becarried out, for example, by applying one or more predetermined formulasthat utilizes the expected amount of processing resources (504) to beconsumed by compressing the data (308, 310, 312) utilizing the datacompression algorithm as an input. Such predetermined formulas may beutilized, for example, to generate a score for each available datacompression algorithm, where the data compression algorithm with thelowest or highest score (depending on the particular construction of thepredetermined formulas) is ultimately selected (506).

Readers will appreciate that when the expected amount of processingresources (504) to be consumed by compressing the data (308, 310, 312)utilizing a particular data compression algorithm is relatively high,such a data compression algorithm may only be selected (506) when theamount of processing resources (322) available in the storage system(300) is also relatively high, in order to avoid performing resourceintensive compression when other consumers of processing resources aredemanding a relatively high amount of processing resources.Alternatively, when the expected amount of processing resources (504) tobe consumed by compressing the data (308, 310, 312) utilizing aparticular data compression algorithm is relatively low, such a dataalgorithm may be selected (506) even when the amount of processingresources (322) available in the storage system (300) is relatively low,as performing such data compression is less likely to prevent otherconsumers of processing resources from accessing such processingresources. In such a way, data compression algorithms may be selectedsuch that performing data compression does not cause other consumers ofprocessing resources to be blocked from accessing such processingresources.

For further explanation, FIG. 6 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 6 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 6 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

The example method depicted in FIG. 6 also includes determining (602),for each of a plurality of data compression algorithms, an expectedamount of data reduction (604) to be achieved by compressing the data(308, 310, 312) utilizing the data compression algorithm. Thecompression prioritization module (314) of FIG. 6 may determine (602) anexpected amount of data reduction (604) to be achieved by compressingthe data (308, 310, 312) utilizing the data compression algorithm, forexample, through the use of historical information gathered whenpreviously compressing data. If the compression prioritization module(314) analyzes historical information gathered when previouslycompressing data to determine, for example, that a first compressionalgorithm historically achieves a data compression rate of 3:1, that asecond compression algorithm historically achieves a data compressionrate of 5:1, and that a third compression algorithm historicallyachieves a data compression rate of 8:1. In such a way, the expectedamount of data reduction (604) to be achieved by compressing the data(308, 310, 312) utilizing the data compression algorithm may bedetermined (602) by assuming that the amount of data to be compressedwill be reduced in a manner that is consistent with the historical datacompression rate achieved by the data compression algorithm.

In the example method depicted in FIG. 6 , determining (602) theexpected amount of data reduction (604) to be achieved by compressingthe data (308, 310, 312) utilizing the data compression algorithm may becarried out by the compression prioritization module (314) receiving oneor more messages describing the historical data compression rateachieved by each data compression algorithm. The one or more messagesdescribing the historical data compression rate achieved by each datacompression algorithm may be received, for example, from a resourcemonitoring module (326) that tracks the performance of variouscompression algorithms or by any other module that is included in thestorage system (300) or operable to otherwise manage the storage system(300). Alternatively, the historical data compression rate achieved byeach data compression algorithm may be tracked by the compressionprioritization module (314) itself.

In the example method depicted in FIG. 6 , the data compressionalgorithm (328) to utilize is further selected (606) in dependence uponthe expected amount of data reduction (604) to be achieved bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm. Selecting (606) a data compression algorithm (328) to utilizeto compress the data (308, 310, 312) in dependence upon the expectedamount of data reduction (604) to be achieved by compressing the data(308, 310, 312) utilizing the data compression algorithm may be carriedout, for example, by applying one or more predetermined formulas thatutilizes the expected amount of data reduction (604) to be achieved bycompressing the data (308, 310, 312) utilizing the data compressionalgorithm as an input. Such predetermined formulas may be utilized, forexample, to generate a score for each available data compressionalgorithm, where the data compression algorithm with the lowest orhighest score (depending on the particular construction of thepredetermined formulas) is ultimately selected (606).

Readers will appreciate that when the expected amount of data reduction(604) to be achieved by compressing the data (308, 310, 312) utilizingthe data compression algorithm is relatively high, such a datacompression algorithm may be selected (606) when the amount of freespace within the storage system (300) is relatively low, even ifexecuting such a data compression algorithm consumes relatively largeamounts of processing resources. Alternatively, when the amount of freespace within the storage system (300) is relatively high, less emphasismay be placed on selecting (606) a data compression algorithm whoseexpected amount of data reduction (604) is relatively high.

For further explanation, FIG. 7 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 7 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 7 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

The example method depicted in FIG. 7 also includes determining (702),for each of a plurality of data compression algorithms, an expecteddecompression speed (704) associated with decompressing the data (308,310, 312). The compression prioritization module (314) of FIG. 7 maydetermine (702) the expected decompression speed (704) associated withdecompressing the data (308, 310, 312) utilizing the data compressionalgorithm, for example, by identifying attributes of the data (308, 310,312) and analyzing historical information gathered when previouslydecompressing data with similar attributes. The attributes of the datacan include information such as, for example, the type of applicationthat initially wrote the data (308, 310, 312) to the storage system(300), the size of the data (308, 310, 312), and so on. In such a way,the expected decompression speed (704) associated with decompressing thedata (308, 310, 312) is specific to the actual data (308, 310, 312)stored on the storage system (300). The expected decompression speed(704) associated with decompressing the data (308, 310, 312) utilizingthe data compression algorithm may be determined (702) by assuming thatthe data (308, 310, 312) will be decompressed at a speed that isconsistent with the historical decompression speed for similar data.

In the example method depicted in FIG. 7 , determining (702) theexpected decompression speed (704) associated with decompressing thedata (308, 310, 312) utilizing the data compression algorithm may becarried out by the compression prioritization module (314) receiving oneor more messages describing the historical decompression speed achievedby each data compression algorithm for different types of data. The oneor more messages describing the historical decompression speed achievedby each data compression algorithm for different types of data may bereceived, for example, from a resource monitoring module (326) thattracks the performance of various compression algorithms or by any othermodule that is included in the storage system (300) or operable tootherwise manage the storage system (300). Alternatively, the historicaldecompression speed achieved by each data compression algorithm fordifferent types of data may be tracked by the compression prioritizationmodule (314) itself.

In the example method depicted in FIG. 7 , the data compressionalgorithm (328) to utilize is further selected (706) in dependence uponthe decompression speed (704) associated with decompressing the data(308, 310, 312). Selecting (706) a data compression algorithm (328) toutilize to compress the data (308, 310, 312) in dependence upon thedecompression speed (704) associated with decompressing the data (308,310, 312) may be carried out, for example, by applying one or morepredetermined formulas that utilizes the decompression speed (704)associated with decompressing the data (308, 310, 312) as an input. Suchpredetermined formulas may be utilized, for example, to generate a scorefor each available data compression algorithm, where the datacompression algorithm with the lowest or highest score (depending on theparticular construction of the predetermined formulas) is ultimatelyselected (706).

Readers will appreciate that when the decompression speed (704)associated with decompressing the data (308, 310, 312) utilizing thedata compression algorithm is relatively high, such a data compressionalgorithm may be selected (706) for data stored by applications thatrequire relatively fast response times, even if utilizing such a datacompression algorithm may result in relatively low levels of datareduction. Alternatively, data stored by applications that do notrequire relatively fast response times may be compressed utilizing datacompression algorithms that deliver relatively high levels of datareduction, data compression algorithm that consume relatively lowamounts of processing resources, and so on, even when the decompressionspeed (704) associated with decompressing the data (308, 310, 312)utilizing the data compression algorithm is relatively low.

For further explanation, FIG. 8 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 8 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 8 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

The example method depicted in FIG. 8 also includes determining (802),for each of a plurality of data compression algorithms, an averagedecompression speed (804) associated with decompressing a pool of data.The compression prioritization module (314) of FIG. 8 may determine(802) the average decompression speed (804) associated withdecompressing a pool of data, for example, by analyzing historicalinformation gathered when previously decompressing data. In such a way,the average decompression speed (804) associated with decompressing apool of data is not specific to the actual data (308, 310, 312) storedon the storage system (300). The average decompression speed (804)associated with decompressing a pool of data utilizing the datacompression algorithm may be determined (802) by assuming that the data(308, 310, 312) will be decompressed at a speed that is consistent withthe historical decompression speed for the data compression algorithm.

In the example method depicted in FIG. 8 , determining (802) averagedecompression speed (804) associated with decompressing a pool of datautilizing the data compression algorithm may be carried out by thecompression prioritization module (314) receiving one or more messagesdescribing the historical decompression speed achieved by each datacompression algorithm. The one or more messages describing thehistorical decompression speed achieved by each data compressionalgorithm may be received, for example, from a resource monitoringmodule (326) that tracks the performance of various compressionalgorithms or by any other module that is included in the storage system(300) or operable to otherwise manage the storage system (300).Alternatively, the historical decompression speed achieved by each datacompression algorithm may be tracked by the compression prioritizationmodule (314) itself.

In the example method depicted in FIG. 8 , the data compressionalgorithm (328) to utilize is further selected (806) in dependence uponthe average decompression speed (804) associated with decompressing apool of data. Selecting (806) a data compression algorithm (328) toutilize to compress the data (308, 310, 312) in dependence upon theaverage decompression speed (804) associated with decompressing a poolof data may be carried out, for example, by applying one or morepredetermined formulas that utilizes the average decompression speed(804) associated with decompressing a pool of data as an input. Suchpredetermined formulas may be utilized, for example, to generate a scorefor each available data compression algorithm, where the datacompression algorithm with the lowest or highest score (depending on theparticular construction of the predetermined formulas) is ultimatelyselected (806).

Readers will appreciate that when the average decompression speed (804)associated with decompressing a pool of data utilizing the datacompression algorithm is relatively high, such a data compressionalgorithm may be selected (806) for data stored by applications thatrequire relatively fast response times, even if utilizing such a datacompression algorithm may result in relatively low levels of datareduction. Alternatively, data stored by applications that do notrequire relatively fast response times may be compressed utilizing datacompression algorithms that deliver relatively high levels of datareduction, data compression algorithm that consume relatively lowamounts of processing resources, and so on, even when averagedecompression speed (804) associated with decompressing a pool of datautilizing the data compression algorithm is relatively low.

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method for compressing data in dependence uponcharacteristics of a storage system (300) according to embodiments ofthe present disclosure. The example method depicted in FIG. 9 is similarto the example method depicted in FIG. 3 , as the example methoddepicted in FIG. 9 also includes receiving (316) an amount of processingresources (322) available in the storage system (300), receiving (318)an amount of space (324) available in the storage system (300), andselecting (320) a data compression algorithm (328) to utilize tocompress the data (308, 310, 312) in dependence upon the priority forconserving the amount of processing resources (322) and the amount ofspace (324).

In the example method depicted in FIG. 9 , selecting (320) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) can include compressing (902) at least a portion of the data (308,310, 312) utilizing a plurality of data compression algorithms.Compressing (902) at least a portion of the data (308, 310, 312)utilizing a plurality of data compression algorithms may be carried out,for example, by compressing the same portion of the data (308, 310, 312)utilizing a plurality of data compression algorithms. Alternatively,compressing (902) at least a portion of the data (308, 310, 312)utilizing a plurality of data compression algorithms may be carried outby compressing a first portion of the data (308, 310, 312) utilizing afirst data compression algorithm, compressing a second portion of thedata (308, 310, 312) utilizing a second data compression algorithm,compressing a third portion of the data (308, 310, 312) utilizing athird data compression algorithm, and so on.

In the example method depicted in FIG. 9 , selecting (320) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) can also include identifying (904) a data reduction level achievedby each data compression algorithm. Identifying (904) a data reductionlevel achieved by each data compression algorithm may be carried out,for example, by determining the original size of the portion of the data(308, 310, 312) that was compressed utilizing a particular datacompression algorithm and also determining the size of the resultantcompressed data that was produced utilizing the particular datacompression algorithm. For example, if the original size of the portionof the data (308, 310, 312) that was compressed utilizing a first datacompression algorithm was 1 MB and the size of the resultant compresseddata that was produced utilizing the first data compression algorithmwas 0.2 MB, the data reduction level achieved by the first datacompression algorithm would be 5:1. Such a calculation may be made foreach data compression algorithm that was utilized to compress (902) atleast a portion of the data (308, 310, 312), such that the datareduction level achieved by each data compression algorithm may beidentified (904).

In the example method depicted in FIG. 9 , selecting (320) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) can also include determining (906) an expected decompression speedassociated with each data compression algorithm. The compressionprioritization module (314) of FIG. 9 may determine (906) the expecteddecompression speed associated with each data compression algorithm, forexample, by identifying attributes of the data (308, 310, 312) andanalyzing historical information gathered when previously decompressingdata with similar attributes. The attributes of the data can includeinformation such as, for example, the type of application that initiallywrote the data (308, 310, 312) to the storage system (300), the size ofthe data (308, 310, 312), and so on. In such a way, the expecteddecompression speed associated with each data compression algorithm maybe specific to the actual data (308, 310, 312) stored on the storagesystem (300). The expected decompression speed associated with each datacompression algorithm may be determined (904) by assuming that the data(308, 310, 312) will be decompressed at a speed that is consistent withthe historical decompression speed for similar data.

In the example method depicted in FIG. 9 , determining (906) an expecteddecompression speed associated with each data compression algorithm maybe carried out by the compression prioritization module (314) receivingone or more messages describing the historical decompression speedachieved by each data compression algorithm for different types of data.The one or more messages describing the historical decompression speedachieved by each data compression algorithm for different types of datamay be received, for example, from a resource monitoring module (326)that tracks the performance of various compression algorithms, by thecompression prioritization module (314) itself, or by any other modulethat is included in the storage system (300) or operable to otherwisemanage the storage system (300).

In the example method depicted in FIG. 9 , selecting (320) a datacompression algorithm (328) to utilize to compress the data (308, 310,312) can also include selecting (908) the data compression algorithm toutilize to compress the data (308, 310, 312) in dependence upon the datareduction level achieved by each data compression algorithm and thedecompression speed associated with each data compression algorithm.Selecting (908) the data compression algorithm to utilize to compressthe data (308, 310, 312) in dependence upon the data reduction levelachieved by each data compression algorithm and the decompression speedassociated with each data compression algorithm may be carried out, forexample, by applying a predetermined formula that utilizes the datareduction level achieved by each data compression algorithm and thedecompression speed associated with each data compression algorithm asinputs. Readers will appreciate that the predetermined formula may beconfigured to strike a balance between the data reduction level achievedby each data compression algorithm and the decompression speedassociated with each data compression algorithm. For example, when theamount of space (324) available in the storage system is relatively low,the compression prioritization module (314) may select (908) to compressthe data (308, 310, 312) using slower, heavier compression algorithmsthat consume relatively large amounts of computing resources and alsoproduce relatively large amounts of data reduction, as a premium will beplaced on data reduction rather than conservation of processingresources as the amount of space available in the storage system isreduced. Alternatively, when the amount of space (324) available in thestorage system is relatively high, a premium may be placed increasingresponse times to I/O operations and, as such, data compressionalgorithms that achieve faster decompression speeds may be selected(908).

In the example embodiments described above, a data compression algorithm(328) to utilize to compress data (308, 310, 312) is selected (320)based on factors such as (but not limited to): the priority forconserving the amount of processing resources (322) and the amount ofspace (324); the amount of processing resources available in the storagesystem; the amount of space available in the storage system; theexpected amount of space in the storage system to be consumed within apredetermined period of time; the expected amount of processingresources to be consumed by compressing the data utilizing the datacompression algorithm; the expected amount of data reduction to beachieved by compressing the data utilizing the data compressionalgorithm; the expected decompression speed associated withdecompressing the data; and the average decompression speed associatedwith decompressing a pool of data. While the example embodimentsdescribed above typically describe selecting a data compressionalgorithm (328) to utilize to compress data (308, 310, 312) based ononly a small subset of such factors, such a description is included onlyfor ease of explanation. Readers will appreciate that embodiments arecontemplated where a data compression algorithm (328) to utilize tocompress data (308, 310, 312) is selected (320) based on combinations offactors not expressly identified above, including utilizing all of thefactors described above to select a data compression algorithm (328) toutilize to compress data (308, 310, 312). In fact, such factors may begiven equal or unequal consideration in various embodiments of thepresent disclosure. Furthermore, a data compression algorithm (328) toutilize to compress data (308, 310, 312) may be selected (320) infurther dependence on additional factors as will occur to those of skillin the art in view of the present disclosure.

Example embodiments of the present disclosure are described largely inthe context of a fully functional computer system. Readers of skill inthe art will recognize, however, that the present disclosure also may beembodied in a computer program product disposed upon computer readablemedia for use with any suitable data processing system. Such computerreadable storage media may be any transitory or non-transitory media.Examples of such media include storage media for machine-readableinformation, including magnetic media, optical media, or other suitablemedia. Examples of such media also include magnetic disks in hard drivesor diskettes, compact disks for optical drives, magnetic tape, andothers as will occur to those of skill in the art. Persons skilled inthe art will immediately recognize that any computer system havingsuitable programming means will be capable of executing the steps of themethod of the invention as embodied in a computer program product.Persons skilled in the art will recognize also that, although some ofthe example embodiments described in this specification are oriented tosoftware installed and executing on computer hardware, nevertheless,alternative embodiments implemented as firmware, as hardware, or as anaggregation of hardware and software are well within the scope ofembodiments of the present disclosure.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present disclosurewithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present disclosure islimited only by the language of the following claims.

What is claimed is:
 1. A method comprising: selecting, in dependenceupon a priority for conserving processing resources or storage resourcesin a storage system, a data compression algorithm to utilize to compressdata; detecting that at least one of an amount of processing resourcesavailable in the storage system or an amount of space available to storeadditional data in the storage system has changed; and responsive todetecting that at least one of the amount of processing resourcesavailable in the storage system or the amount of space available tostore additional data in the storage system has changed, selecting adifferent data compression algorithm to utilize to compress data.
 2. Themethod of claim 1 further comprising: determining an expected amount ofspace in the storage system to be consumed within a predetermined periodof time; and wherein the data compression algorithm to utilize isfurther selected in dependence upon the expected amount of space in thestorage system to be consumed within the predetermined period of time.3. The method of claim 1 further comprising: determining, for each of aplurality of data compression algorithms, an expected amount ofprocessing resources to be consumed by compressing the data utilizingthe data compression algorithm; and wherein the data compressionalgorithm to utilize is further selected in dependence upon the expectedamount of processing resources to be consumed by compressing the datautilizing the data compression algorithm.
 4. The method of claim 1further comprising: determining, for each of a plurality of datacompression algorithms, an expected amount of data reduction to beachieved by compressing the data utilizing the data compressionalgorithm; and wherein the data compression algorithm to utilize isfurther selected in dependence upon the expected amount of datareduction to be achieved by compressing the data utilizing the datacompression algorithm.
 5. The method of claim 1 further comprising:determining, for each of a plurality of data compression algorithms, anexpected decompression speed associated with decompressing the data; andwherein the data compression algorithm to utilize is further selected independence upon the expected decompression speed associated withdecompressing the data.
 6. The method of claim 1 further comprising:determining, for each of a plurality of data compression algorithms, anaverage decompression speed associated with decompressing a pool ofdata; and wherein the data compression algorithm to utilize is furtherselected in dependence upon the average decompression speed associatedwith decompressing a pool of data.
 7. The method of claim 1 whereinselecting the data compression algorithm to utilize to compress the datafurther comprises: compressing at least a portion of the data utilizinga plurality of data compression algorithms; identifying a data reductionlevel achieved by each data compression algorithm; determining adecompression speed associated with each data compression algorithm; andselecting the data compression algorithm to utilize to compress the datain dependence upon the data reduction level achieved by each datacompression algorithm and the decompression speed associated with eachdata compression algorithm.
 8. An apparatus, the apparatus including acomputer processor and a computer memory, the computer memory includingcomputer program instructions that, when executed, cause the apparatusto carry out the steps of: selecting, in dependence upon a priority forconserving processing resources or storage resources in a storagesystem, a data compression algorithm to utilize to compress data;detecting that at least one of an amount of processing resourcesavailable in the storage system or the amount of space available tostore additional data in the storage system has changed; and responsiveto detecting that at least one of the amount of processing resourcesavailable in the storage system or the amount of space available tostore additional data in the storage system has changed, selecting adifferent data compression algorithm to utilize to compress data.
 9. Theapparatus of claim 8 further comprising computer program instructionsthat, when executed, cause the apparatus to carry out the step of:determining an expected amount of space in the storage system to beconsumed within a predetermined period of time; and wherein the datacompression algorithm to utilize is further selected in dependence uponthe expected amount of space in the storage system to be consumed withinthe predetermined period of time.
 10. The apparatus of claim 8 furthercomprising computer program instructions that, when executed, cause theapparatus to carry out the step of: determining, for each of a pluralityof data compression algorithms, an expected amount of processingresources to be consumed by compressing the data utilizing the datacompression algorithm; and wherein the data compression algorithm toutilize is further selected in dependence upon the expected amount ofprocessing resources to be consumed by compressing the data utilizingthe data compression algorithm.
 11. The apparatus of claim 8 furthercomprising computer program instructions that, when executed, cause theapparatus to carry out the step of: determining, for each of a pluralityof data compression algorithms, an expected amount of data reduction tobe achieved by compressing the data utilizing the data compressionalgorithm; and wherein the data compression algorithm to utilize isfurther selected in dependence upon the expected amount of datareduction to be achieved by compressing the data utilizing the datacompression algorithm.
 12. The apparatus of claim 8 further comprisingcomputer program instructions that, when executed, cause the apparatusto carry out the step of: determining, for each of a plurality of datacompression algorithms, an expected decompression speed associated withdecompressing the data; and wherein the data compression algorithm toutilize is further selected in dependence upon the expecteddecompression speed associated with decompressing the data.
 13. Theapparatus of claim 8 further comprising computer program instructionsthat, when executed, cause the apparatus to carry out the step of:determining, for each of a plurality of data compression algorithms, anaverage decompression speed associated with decompressing a pool ofdata; and wherein the data compression algorithm to utilize is furtherselected in dependence upon the average decompression speed associatedwith decompressing a pool of data.
 14. The apparatus of claim 8 whereinselecting the data compression algorithm to utilize to compress the datafurther comprises: compressing at least a portion of the data utilizinga plurality of data compression algorithms; identifying a data reductionlevel achieved by each data compression algorithm; determining adecompression speed associated with each data compression algorithm; andselecting the data compression algorithm to utilize to compress the datain dependence upon the data reduction level achieved by each datacompression algorithm and the decompression speed associated with eachdata compression algorithm.
 15. A computer program product, the computerprogram product disposed on a non-transitory computer readable storagemedium, the computer program product comprising computer programinstructions that, when executed, cause an apparatus to carry out thesteps of: selecting, in dependence upon a priority for conservingprocessing resources or storage resources in a storage system, a datacompression algorithm to utilize to compress data; detecting that atleast one of an amount of processing resources available in the storagesystem or the amount of space available to store additional data in thestorage system has changed; and responsive to detecting that at leastone of the amount of processing resources available in the storagesystem or the amount of space available to store additional data in thestorage system has changed, selecting a different data compressionalgorithm to utilize to compress data.
 16. The computer program productof claim 15 further comprising computer program instructions that, whenexecuted, cause the apparatus to carry out the step of: determining anexpected amount of space in the storage system to be consumed within apredetermined period of time; and wherein the data compression algorithmto utilize is further selected in dependence upon the expected amount ofspace in the storage system to be consumed within the predeterminedperiod of time.
 17. The computer program product of claim 15 furthercomprising computer program instructions that, when executed, cause theapparatus to carry out the step of: determining, for each of a pluralityof data compression algorithms, an expected amount of processingresources to be consumed by compressing the data utilizing the datacompression algorithm; and wherein the data compression algorithm toutilize is further selected in dependence upon the expected amount ofprocessing resources to be consumed by compressing the data utilizingthe data compression algorithm.
 18. The computer program product ofclaim 15 further comprising computer program instructions that, whenexecuted, cause the apparatus to carry out the step of: determining, foreach of a plurality of data compression algorithms, an expected amountof data reduction to be achieved by compressing the data utilizing thedata compression algorithm; and wherein the data compression algorithmto utilize is further selected in dependence upon the expected amount ofdata reduction to be achieved by compressing the data utilizing the datacompression algorithm.
 19. The computer program product of claim 15further comprising computer program instructions that, when executed,cause the apparatus to carry out the step of: determining, for each of aplurality of data compression algorithms, an expected decompressionspeed associated with decompressing the data; and wherein the datacompression algorithm to utilize is further selected in dependence uponthe expected decompression speed associated with decompressing the data.20. The computer program product of claim 15 further comprising computerprogram instructions that, when executed, cause the apparatus to carryout the step of: determining, for each of a plurality of datacompression algorithms, an average decompression speed associated withdecompressing a pool of data; and wherein the data compression algorithmto utilize is further selected in dependence upon the averagedecompression speed associated with decompressing a pool of data.